research-article
Authors: Shuo Mao, Pengfei Leng, and Chunhui Liu
CVIPPR '24: Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 2024
Article No.: 12, Pages 1 - 5
Published: 27 June 2024 Publication History
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Abstract
ShipRSImageNet is a large-scale, fine-grained ship detection dataset specifically designed for high-resolution optical remote sensing images. Existing methods rely on traditional object detection techniques that involve Non-Maximum Suppression (NMS) post-processing. However, the groundbreaking DETR (Detection Transformer) introduced an end-to-end approach to object detection. While Sparse R-CNN improved upon this, it still faced challenges related to low accuracy and slow convergence.
In this paper, we propose an Enhanced Sparse R-CNN that achieves full end-to-end capabilities. We introduce the Adaptive Proposal Network, designed to generate high-quality proposal boxes and proposal features. Additionally, we propose the Prediction Update Network, which refines the detection boxes generated by Sparse R-CNN. By comparing our method’s performance on the ShipRSImageNet dataset, we demonstrate its superiority in fine-grained ship detection within high-resolution optical remote sensing images. Our approach not only enhances detection accuracy but also effectively handles complex environments, small ships, and fine-grained classification. We believe this research has practical implications for the field of object detection in high-resolution optical remote sensing images using deep learning.
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Index Terms
Advancing Fine-Grained Ship Detection: An Enhanced End-to-End Approach
Computing methodologies
Artificial intelligence
Computer vision
Computer vision tasks
Visual content-based indexing and retrieval
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CVIPPR '24: Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2024
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Author Tags
- end-to-end detection
- fine-grained detection
Qualifiers
- Research-article
- Research
- Refereed limited
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CVIPPR 2024
CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 26 - 28, 2024
Xiamen, China
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Overall Acceptance Rate 14 of 38 submissions, 37%
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